Abstract P1-05-18: Orphan non-coding RNAs for early detection of breast cancer with liquid biopsy
Authors: Taylor B. Cavazos, Jeffrey Wang, Oluwadamilare I. Afolabi, Alice Huang, Dung Ngoc Lam, Seda Kilinc, Jieyang Wang, Lisa Fish, Xuan Zhao, Andy Pohl, Helen Li, Kimberly H. Chau, Patrick A. Arensdorf, Fereydoun Hormozdiari, Hani Goodarzi, Babak Alipanahi
Journal: Cancer Research (2023)

Abstract Background: Early detection of breast cancer is crucial for optimal patient outcomes but cannot always be accomplished based on symptoms or screening mammography. Biomarker-based screening could aid early detection of breast cancer by improving sensitivity and specificity. Exai Bio has developed a novel liquid biopsy technology that detects and analyzes small non-coding RNAs that are cancer specific, termed orphan non-coding RNAs (oncRNAs). Previous work in patients with diagnosed breast cancer demonstrated that changes in oncRNAs in serum reflected treatment response and event-free survival. In this study, we developed an assay that measures oncRNAs in serum to detect breast cancer across the range of tumor stages and sizes. Methods: Previously, a library of ~260,000 oncRNAs from 32 different cancers was compiled based on smRNA sequences found in tumor tissues and largely absent in tumor-adjacent normal tissues from The Cancer Genome Atlas (TCGA). To refine this library for applications in serum, we sequenced smRNA in 31 control serum samples. These smRNA sequences were filtered from the larger library, reducing its size to 250,332 oncRNAs. The diagnostic performance of these oncRNAs was then assessed in an independent cohort of archived serum samples from 96 female patients with clinically diagnosed, untreated breast cancer and 95 age- and sex-matched individuals with no known history of cancer. We sequenced smRNAs at an average depth of 17.7 million 50-bp single-end reads per sample. Of the 250,332 oncRNAs in our library, 171,981 (68.7%) were detected in our independent study cohort. An ensemble of logistic regression models was trained with 5-fold cross-validation, using only those oncRNAs yielding an odds ratio &amp;gt;1 and observed in &amp;gt;6% of samples within each training set. Results: The cohort of 96 breast cancer patients and 95 matched controls had mean ages of 59.4 and 56.3 years, respectively. Area under the receiver operating characteristic curve (AUC) for detecting breast cancer was 0.94 (95% CI, 0.85–0.96). Sensitivities for detecting breast cancer at 95% specificity ranged from 0.75 to 0.87 among the four breast cancer stages, including a sensitivity of 0.81 for tumor stage I (Table 1); and from 0.67 to 0.87 among the four main TNM T categories (Table 2). Sensitivities at 95% specificity were relatively high for small tumors, at 0.75 (95% CI, 0.40–0.97) for T1b (&amp;gt;5mm to ≤10mm; n = 9) and 0.80 (0.68–0.94) for T1c (&amp;gt;10mm to ≤20mm; n = 37). Conclusions We have demonstrated the potential value of an oncRNA-based liquid biopsy assay by showing that oncRNAs can be used to detect breast cancer in serum samples with high sensitivity, and that detection requires fewer reads than are needed with other platforms. Moreover, we found that this oncRNA-based assay performed well in detecting early-stage breast cancer and small tumors. This suggests that an oncRNA-based liquid biopsy assay may be beneficial for early detection of breast cancer. Table 1. Model sensitivity by tumor stage. For the indicated numbers of cases (N), sensitivity and Pearson-Clopper 95% confidence intervals are reported for tumor detection by the oncRNA-based model at 95% specificity by tumor stage, as defined by the AJCC 7th Edition breast cancer staging system. Table 2. Model sensitivity by tumor size. For the indicated numbers of cases (N), sensitivity and Pearson-Clopper 95% confidence intervals are reported for tumor detection by the oncRNA-based model at 95% specificity by TNM T category, as defined by the AJCC 7th Edition breast cancer staging system. Citation Format: Taylor B. Cavazos, Jeffrey Wang, Oluwadamilare I. Afolabi, Alice Huang, Dung Ngoc Lam, Seda Kilinc, Jieyang Wang, Lisa Fish, Xuan Zhao, Andy Pohl, Helen Li, Kimberly H. Chau, Patrick A. Arensdorf, Fereydoun Hormozdiari, Hani Goodarzi, Babak Alipanahi. Orphan non-coding RNAs for early detection of breast cancer with liquid biopsy [abstract]. In: Proceedings of the 2022 San Antonio Breast Cancer Symposium; 2022 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2023;83(5 Suppl):Abstract nr P1-05-18.